Conditions and limitations of digital satellite image pre-processing for the further 3D modeling

• Autorzy: Hnatushenko V. V. , Kavats O. O. , Kubanek M. , Kibukevych Y. O.

Identyfikatory

DOI

10.17512/jamcm.2016.3.06

• Języki publikacji: EN

Abstrakty

EN

Today, 3D models of complex urban buildings are one of the most popular applications of 3D modeling. 3D models of complex urban buildings provide high data interpretation that accurately transfers information about objects or area changes and allows one to solve a number of applied tasks. The quality of the 3D models depends on the quality of the initial images and the method of the object recognition. First of all, the 3D-model building requires identification and classification building borders, which requires determination of the building roof form. The article reviews the existing classification and recognition methods for the 3D further modeling.

Słowa kluczowe

EN

photogrammetric images; classification; segmentation; building detection; 3D models

PL

obraz fotogrametryczny; detekcja budynku; detekcja dachu; model 3D; segmentacja

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2016
• Tom: Vol. 15, nr 3
• Strony: 57--65
• Opis fizyczny: Bibliogr. 10 poz. rys., tab.

Twórcy

autor

Hnatushenko V. V.

• O. Honchar Dnepropetrovsk National University Dnepropetrovsk, Ukraine

autor

Kavats O. O.

• National Metallurgical Academy of Ukraine Dnepropetrovsk, Ukraine

autor

Kubanek M.

• Institute of Computer and Information Science, Czestochowa University of Technology Częstochowa, Poland

autor

Kibukevych Y. O.

• National Metallurgical Academy of Ukraine Dnepropetrovsk, Ukraine
• Institute of Computer and Information Science, Czestochowa University of Technology Częstochowa, Poland

Bibliografia

• [1] Lu D., Weng Q., A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing 2007, 28, 823-870.
• [2] Tso B., Mather P.M., Classification Methods for Remotely Sensed Data, Taylor and Francis Inc., New York 2001.
• [3] Hnatushenko V.V., Hnatushenko Vik.V., Kavats A.A., Shevchenko V.Ju., Pansharpening technology of high resolution multispectral and panchromatic satellite images, Scientific Bulletin of National Mining University, State Higher Educational Institution “National Mining University”, Dnipropetrovsk 2015, 4(148), 91-98.
• [4] Hnatushenko V.V., Kavats О.О., Kibukevych I.O., Efficiency Determination of Scanner Data Fusion Methods of Space Multispectral Images, International Young Scientists Forum on Applied Physics «YSF-2015», September 29 - October 2, 2015, Dnipropetrovsk 2015.
• [5] Hnatushenko V., Kavats A., Information technology increase spatial fragmentation of digital satellite images based on wavelet transformation and IСA, Proceedings of the National University “Lviv Polytechnic” series “Computer Science and Information Technology”, Lviv 2013, 28-32.
• [6] Hay G.J., Castilla G., Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT), International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 2006, 36(4).
• [7] Li M., Zang S., Zhang B., Li S., Wu C., A review of remote sensing image classification techniques: The role of spatio-contextual information, European Journal of Remote Sensing 2014, 47, 389-411.
• [8] Pacifici F., Chini M., Emery W.J., A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification, Remote Sensing of Environment 2009, 113, 1276-1292.
• [9] Zhen Z., Quackenbush L.J., Stehman S.V., Zhang L., Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification, International Journal of Remote Sensing 2013, 34(19), 6914-6930.
• [10] Zeng C., Wang J., Automated Building Information Extraction and Evaluation from Highresolution Remotely Sensed Data, The University of Western Ontario, Electronic Thesis and Dissertation Repository, 2014.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.